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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

A Quantum Bi-Directional Self-Organizing Neural Network (QBDSONN) for binary image denoising

Debanjan Konar, Siddhartha Bhattacharyya, Nibaran Das, Bijaya Ketan Panigrahi

Published Aug 1, 2015• 12 citations
Publisher

Abstract

A Quantum Bi-directional Self-Organizing Neural Network (QBDSONN) architecture suitable for binary image denoising in real time is proposed in this article. It is composed of three second order neighborhood topology based interconnected layers of neurons (represented by qubits) known as input, intermediate and output layers. Moreover, it does not use any quantum back-propagation algorithm for the adjustment of its interconnection weights. Instead, it resorts to a counter-propagation of quantum states of the intermediate layer and the output layer. In the proposed architecture, the inter-connection weights and activation values are represented by rotation gates. The quantum neurons of each network layer follow a cellular network pattern and are fully intra-connected to each other. QBDSONN self-organizes the quantized input image information by means of the counter-propagating fashion of the quantum network states of the intermediate and output layers of the architecture. A quantum measurement at the output layer collapses superposition of quantum states of the processed information thereby yielding the desired outputs once the network attains stability. Applications of QBDSONN are demonstrated on the denoising of a synthetic and real life spanner image with different degrees of uniform noise and Gaussian noise. Comparative results indicate that QBDSONN outperforms its classical counterpart in terms of time and also it retains the shapes of the denoised images with great precision.

DOI
10.1109/icacci.2015.7275780

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A Quantum Bi-directional Self-Organizing Neural Network (QBDSONN) architecture suitable for binary image denoising in real time is proposed in this article. It is composed of three second order neighborhood topology based interconnected layers of neurons (represented by qubits) known as input, intermediate and output layers. Moreover, it does not use any quantum back-propagation algorithm for the adjustment of its interconnection weights. Instead, it resorts to a counter-propagation of quantum states of the intermediate layer and the output layer. In the proposed architecture, the inter-connection weights and activation values are represented by rotation gates. The quantum neurons of each network layer follow a cellular network pattern and are fully intra-connected to each other. QBDSONN self-organizes the quantized input image information by means of the counter-propagating fashion of the quantum network states of the intermediate and output layers of the architecture. A quantum measurement at the output layer collapses superposition of quantum states of the processed information thereby yielding the desired outputs once the network attains stability. Applications of QBDSONN are demonstrated on the denoising of a synthetic and real life spanner image with different degrees of uniform noise and Gaussian noise. Comparative results indicate that QBDSONN outperforms its classical counterpart in terms of time and also it retains the shapes of the denoised images with great precision.
This paper is aimed to propose a suitable real-time pure color image denoising procedure using a self-supervised network referred to as Quantum Parallel Bi-directional Self-Organizing Neural Network (QPBDSONN) architecture. The proposed QPBDSONN replicates the Parallel Bi-directional Self-Organizing Neural Network (PBDSONN) architecture and exploits by the power of quantum computation. To process three distinct basic color components (Red, Green and Blue) of noisy color image, QPBDSONN uses trinity of Quantum Bi-directional Self-organizing Neural Network (QBDSONN) architecture at the source layer in parallel mode. Each constituent QBDSONN comprises input, intermediate or hidden and output layers interconnected by 8-connected neighborhood topology based layer of neurons represented by qubits. Each constituent QBDSONN updates weighted interconnections in the form of quantum states through counter-propagation between hidden and output layer to obviate quantum back propagation. Rotation gates are introduced to represent weighted inter-links and values of activation. Finally, a quantum measurement operation is performed at the output layer of each constituent QBDSONN followed by fusion operation at the sink layer of QPBDSONN to concatenate the processed color image components resulting in the true output. The superiority of the proposed network architecture over the classical PBDSONN can be established using a real-life spanner pure color image and synthetic pure color image corrupted with various intensity of uniform and Gaussian noise in terms of extraction time and shapes.
Binary object extraction from a noisy perspective is a challenging proposition in the computer vision community. Various research initiatives based on different methodologies have been entrusted on this aspect over the decades. The multilayer self-organizing neural network (MLSONN) architecture implemented by a fuzzy measure guided backpropagation of errors is one of the best accomplishments in this direction. In this article, a quantum multilayer self-organizing neural network (QMLSONN) architecture is proposed to achieve the same objective. Quantum computation plays a remarkable role in the proposed architecture. The proposed architecture operates using single qubit rotation gates. Here different nodes and interconnection weights act as vital components for this intention. The proposed QMLSONN architecture comprises three processing layers specified as input, hidden and output layers. The nodes in the processing layers are represented by qubits and the interconnection weights are represented by quantum gates. A quantum measurement at the output layer destroys the quantum states of the processed information thereby inducing assimilation of linear indices of fuzziness as the network system errors used to adjust network interconnection weights through a proposed quantum back propagation algorithm. Results of application of the QMLSONN are established on a synthetic and a real life spanner image with various degrees of Gaussian noise and uniform noise. Comparative study with the classical MLSONN architecture reveals the time efficiency of the presented QMLSONN architecture. Moreover, the QMLSONN architecture also restores the shape of the extracted objects to a great extent.
Proper extraction of objects from a noisy perspective is an upheaval task in the computer vision research community. Several intelligent research paradigms have been focused on this aspect over the years. Notable among them is the multilayer self organizing neural network (MLSONN) architecture assisted by fuzzy measure guided back propagation of errors. In this article, we propose a quantum version of the MLSONN architecture which operates using single qubit rotation gates. The proposed QMLSONN architecture comprises three processing layers viz., input, hidden and output layers. The nodes of the processing layers are represented by qubits and the interconnection weights are represented by quantum gates. A quantum measurement at the output layer destroys the quantum states of the processed information thereby inducing incorporation of linear indices of fuzziness as the network system errors used to adjust network interconnection weights through a proposed quantum back propagation algorithm. Results of application of the QMLSONN are demonstrated on a synthetic and a real life spanner image with various degrees of Gaussian noise. A comparative study with the performance of the classical MLSONN architecture reveals the time efficiency of the proposed QMLSONN architecture.
This chapter proposes a quantum multi-layer neural network (QMLNN) architecture suitable for handwritten character recognition in real time, assisted by quantum backpropagation of errors calculated from the quantum-inspired fuzziness measure of network output states. It is composed of three second-order neighborhood-topology-based inter-connected layers of neurons represented by qubits known as input, hidden, and output layers. The QMLNN architecture is a feed forward network with standard quantum backpropagation algorithm for the adjustment of its weighted interconnection. QMLNN self-organizes the quantum fuzzy input image information by means of the quantum backpropagating errors at the intermediate and output layers of the architecture. The interconnection weights are described using rotation gates. After the network is stabilized, a quantum observation at the output layer destroys the superposition of quantum states in order to obtain true binary outputs.
In this article, a quantum version of the parallel self organizing neural network (QPSONN) architecture for extraction of pure color objects from a noisy perspective is proposed. The QPSONN architecture operates in a phased manner to process input noisy pure color images. After the segregation of the pure color inputs into pure color components in the initial phase, these components are subsequently forwarded for processing to three component quantum multilayer self organizing neural network (QMLSONN) architectures composed of three processing layers viz., input, hidden and output layers characterized by qubits based neurons. The interconnection weights are represented by single qub it rotation gates. Quantum measurements at the component output layers destroy the quantum states of the processed information facilitating adjustment of network interconnection weights by a quantum back propagation algorithm using linear indices of fuzziness. Finally, a fusion of the stable component outputs are brought about in a sink layer to produce extracted outputs. Results of application of the QPSONN are demonstrated on a synthetic and a real life spanner image with various degrees of Gaussian noise. A comparison with the classical PSONN architecture reveals the extraction and time efficiency of the proposed QPSONN architecture.
This chapter proposes a quantum multi-layer neural network (QMLNN) architecture suitable for handwritten character recognition in real time, assisted by quantum backpropagation of errors calculated from the quantum-inspired fuzziness measure of network output states. It is composed of three second-order neighborhood-topology-based inter-connected layers of neurons represented by qubits known as input, hidden, and output layers. The QMLNN architecture is a feed forward network with standard quantum backpropagation algorithm for the adjustment of its weighted interconnection. QMLNN self-organizes the quantum fuzzy input image information by means of the quantum backpropagating errors at the intermediate and output layers of the architecture. The interconnection weights are described using rotation gates. After the network is stabilized, a quantum observation at the output layer destroys the superposition of quantum states in order to obtain true binary outputs.